Multi-table entity matching (MEM) addresses the limitations of dual-table approaches by enabling simultaneous identification of equivalent entities across multiple data sources without unique identifiers. However, existing methods relying on pre-trained language models struggle to handle semantic inconsistencies caused by numerical attribute variations. Inspired by the powerful language understanding capabilities of large language models (LLMs), we propose a novel LLM-based framework for multi-table entity matching, termed LLM4MEM. Specifically, we first propose a multi-style prompt-enhanced LLM attribute coordination module to address semantic inconsistencies. Then, to alleviate the matching efficiency problem caused by the surge in the number of entities brought by multiple data sources, we develop a transitive consensus embedding matching module to tackle entity embedding and pre-matching issues. Finally, to address the issue of noisy entities during the matching process, we introduce a density-aware pruning module to optimize the quality of multi-table entity matching. We conducted extensive experiments on 6 MEM datasets, and the results show that our model improves by an average of 5.1% in F1 compared with the baseline model. Our code is available at https://github.com/Ymeki/LLM4MEM.

翻译：多元表实体匹配（MEM）通过无需唯一标识符即可跨多个数据源同时识别等价实体，克服了双表方法的局限性。然而，依赖预训练语言模型的现有方法难以处理由数值属性变化引起的语义不一致性。受大语言模型（LLM）强大语言理解能力的启发，我们提出了一种基于LLM的新型多元表实体匹配框架，称为LLM4MEM。具体而言，我们首先提出一种多风格提示增强的LLM属性协调模块以解决语义不一致问题。接着，为缓解多数据源带来的实体数量激增导致的匹配效率问题，我们开发了一种传递性共识嵌入匹配模块，以处理实体嵌入和预匹配问题。最后，针对匹配过程中的噪声实体问题，我们引入密度感知剪枝模块以优化多元表实体匹配质量。我们在6个MEM数据集上进行了广泛实验，结果表明，与基准模型相比，我们的模型F1值平均提升5.1%。我们的代码见https://github.com/Ymeki/LLM4MEM。